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Record W2112365419 · doi:10.1071/aseg2001ab048

Spectral component analysis applied to portable gamma ray spectrometry

2001· article· en· W2112365419 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueASEG Extended Abstracts · 2001
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsTransCanada (Canada)
FundersInternational Atomic Energy AgencyBruce Power
KeywordsDetectorThoriumSpectrometerNoise (video)Gamma ray spectrometryUraniumSampling (signal processing)Sodium iodideGamma spectroscopyAnalytical Chemistry (journal)Remote sensingEnvironmental scienceOpticsPhysicsChemistryRadiochemistryGeologyNuclear physicsComputer science

Abstract

fetched live from OpenAlex

Spectral component analysis is commonly applied to airborne gamma ray spectral data to reduce statistical noise in the measurements of potassium, uranium and thorium. This technique has been applied to continuous 5-second ground measurements with a portable gamma ray spectrometer using a 7.6 cm x 7.6 cm (3 inch x 3 inch) sodium iodide detector. The results have shown that the reduction in statistical noise is much greater than for large airborne detectors. Even with a short sampling time of 5-seconds, continuous ground measurements have practical significance for both geological mapping and mineral exploration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.231
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it